Fares Rabih, Atlan Lilian D, Druckmann Ido, Factor Shai, Gortzak Yair, Segal Ortal, Artzi Moran, Sternheim Amir
Department of Radiology, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel.
Division of Orthopedics, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel.
J Imaging. 2024 May 17;10(5):122. doi: 10.3390/jimaging10050122.
Desmoid tumors (DTs) are non-metastasizing and locally aggressive soft-tissue mesenchymal neoplasms. Those that become enlarged often become locally invasive and cause significant morbidity. DTs have a varied pattern of clinical presentation, with up to 50-60% not growing after diagnosis and 20-30% shrinking or even disappearing after initial progression. Enlarging tumors are considered unstable and progressive. The management of symptomatic and enlarging DTs is challenging, and primarily consists of chemotherapy. Despite wide surgical resection, DTs carry a rate of local recurrence as high as 50%. There is a consensus that contrast-enhanced magnetic resonance imaging (MRI) or, alternatively, computerized tomography (CT) is the preferred modality for monitoring DTs. Each uses Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), which measures the largest diameter on axial, sagittal, or coronal series. This approach, however, reportedly lacks accuracy in detecting response to therapy and fails to detect tumor progression, thus calling for more sophisticated methods. The objective of this study was to detect unique features identified by deep learning that correlate with the future clinical course of the disease. Between 2006 and 2019, 51 patients (mean age 41.22 ± 15.5 years) who had a tissue diagnosis of DT were included in this retrospective single-center study. Each had undergone at least three MRI examinations (including a pretreatment baseline study), and each was followed by orthopedic oncology specialists for a median of 38.83 months (IQR 44.38). Tumor segmentations were performed on a T2 fat-suppressed treatment-naive MRI sequence, after which the segmented lesion was extracted to a three-dimensional file together with its DICOM file and run through deep learning software. The results of the algorithm were then compared to clinical data collected from the patients' medical files. There were 28 males (13 stable) and 23 females (15 stable) whose ages ranged from 19.07 to 83.33 years. The model was able to independently predict clinical progression as measured from the baseline MRI with an overall accuracy of 93% (93 ± 0.04) and ROC of 0.89 ± 0.08. Artificial intelligence may contribute to risk stratification and clinical decision-making in patients with DT by predicting which patients are likely to progress.
硬纤维瘤（DTs）是一种非转移性且具有局部侵袭性的软组织间叶肿瘤。那些增大的硬纤维瘤往往会发生局部侵袭并导致严重的发病率。硬纤维瘤具有多种临床表现形式，高达50 - 60%的患者在诊断后肿瘤不再生长，20 - 30%的患者在初始进展后肿瘤缩小甚至消失。增大的肿瘤被认为是不稳定且进行性发展的。有症状且增大的硬纤维瘤的治疗具有挑战性，主要治疗方法为化疗。尽管进行了广泛的手术切除，硬纤维瘤的局部复发率仍高达50%。目前的共识是，对比增强磁共振成像（MRI）或者计算机断层扫描（CT）是监测硬纤维瘤的首选方式。每种方式都使用实体瘤疗效评价标准第1.1版（RECIST 1.1），该标准测量轴位、矢状位或冠状位序列上的最大直径。然而，据报道这种方法在检测治疗反应方面缺乏准确性，并且无法检测肿瘤进展，因此需要更复杂的方法。本研究的目的是检测通过深度学习识别出的与疾病未来临床进程相关的独特特征。在2006年至2019年期间，51例经组织学诊断为硬纤维瘤的患者（平均年龄41.22±15.5岁）被纳入这项回顾性单中心研究。每位患者至少接受了三次MRI检查（包括治疗前的基线研究），并且由骨肿瘤专科医生进行随访，中位随访时间为38.83个月（四分位间距44.38）。在T2脂肪抑制的未治疗MRI序列上进行肿瘤分割，之后将分割后的病变与其DICOM文件一起提取到三维文件中，并通过深度学习软件进行处理。然后将算法的结果与从患者病历中收集的临床数据进行比较。有28名男性（13名病情稳定）和23名女性（15名病情稳定），年龄范围为19.07至83.33岁。该模型能够从基线MRI独立预测临床进展，总体准确率为93%（93±0.04），ROC为0.89±0.08。人工智能通过预测哪些患者可能会进展，可能有助于硬纤维瘤患者的风险分层和临床决策。
